Eiko Yoneki

Eiko Yoneki is a Senior Researcher and Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology. She holds a Ph.D. in Computer Science from Cambridge (2007) and a Postgraduate Diploma (2003). Her research focuses on distributed systems, networking, databases, and large-scale graph processing, with a recent emphasis on auto-tuning frameworks using machine learning techniques like Bayesian Optimization and Reinforcement Learning. She has extensive industry experience, including roles at IBM across multiple countries.

• Affiliations: Department of Computer Science and Technology, University of Cambridge
• Education: Ph.D. (2007), Postgraduate Diploma (2003) in Computer Science, University of Cambridge

Her research interests span system autotuning, probabilistic performance modeling, and scalable graph analytics. Notable projects include hierarchical Bayesian optimization for high-dimensional parameter spaces and reinforcement learning for GPU schedule optimization. She has contributed to frameworks like RLFlow and HiBO, advancing efficient resource management in compute systems.

Labs/Teams: Leads research groups focused on machine learning-driven system optimization and large-scale graph processing. Collaborates with industry partners on cloud computing efficiency and hardware-aware algorithms.

Grants/Advising: No specific grants listed, but her work is supported by academic and industrial collaborations.